Actively Recruiting
Multimodal Deep Learning for Predicting Treatment Response to Neoadjuvant Chemoimmunotherapy in Esophageal Cancer
Led by Central South University · Updated on 2026-05-12
200
Participants Needed
1
Research Sites
52 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
This observational study aims to investigate a clinical cohort of patients with locally advanced esophageal cancer undergoing neoadjuvant chemoimmunotherapy. By integrating multimodal clinical data-including demographic characteristics, medical history, imaging studies, pathological findings, and laboratory tests-and employing deep learning algorithms, the study seeks to develop predictive models for the early and accurate assessment of treatment response prior to surgery. Specifically, this study focuses on addressing the following key scientific questions: 1. Can multimodal clinical data be used to construct an accurate model for predicting pathological complete response (pCR) following neoadjuvant therapy? 2. Can deep learning models enable early identification of patients with suboptimal response to neoadjuvant therapy, defined as stable disease (SD) or progressive disease (PD), before surgery?
CONDITIONS
Official Title
Multimodal Deep Learning for Predicting Treatment Response to Neoadjuvant Chemoimmunotherapy in Esophageal Cancer
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Patients with histologically confirmed esophageal cancer based on biopsy results
- Patients recommended for neoadjuvant chemoimmunotherapy following multidisciplinary team discussion or thoracic surgery specialist evaluation
- Patients who have received neoadjuvant chemoimmunotherapy
- Patients with complete imaging data before and after neoadjuvant treatment
You will not qualify if you...
- Patients who are eligible for surgery but refuse surgical treatment
- Patients with missing or poor-quality CT images
- Patients with other concurrent malignancies besides esophageal cancer
- Patients with incomplete clinical data
AI-Screening
AI-Powered Screening
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Trial Site Locations
Total: 1 location
1
The Second Xiangya Hospital of Central South University
Changsha, Hunan, China, 410011
Actively Recruiting
Research Team
C
Chen Chen
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
4
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